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 "cells": [
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    "# Understanding Hierarchical Actor-Critic (HAC): A Complete Guide\n",
    "\n",
    "### Important Note:\n",
    "\n",
    "It contains some `bugs` and is not fully functional. The code is provided for educational purposes and may require modifications to work correctly. The focus is on the conceptual understanding of HAC and its implementation in a custom multi-goal grid world environment.\n",
    "\n",
    "# Table of Contents\n",
    "\n",
    "- [Introduction: Hierarchy in Reinforcement Learning](#introduction)\n",
    "- [What is HAC?](#what-is-hac)\n",
    "  - [Core Idea: Levels of Abstraction & Goal Setting](#core-idea)\n",
    "  - [Key Mechanisms: Time Limits, Intrinsic Rewards, Hindsight](#key-mechanisms)\n",
    "- [Why Hierarchical RL? Advantages](#why-hrl)\n",
    "- [Where and How HAC is Used](#where-and-how-hac-is-used)\n",
    "- [Mathematical Foundation of HAC (Conceptual)](#mathematical-foundation-of-hac)\n",
    "  - [Multi-Level Policies & Value Functions](#multi-level-policies--value-functions)\n",
    "  - [Goal-Conditioned Learning](#goal-conditioned-learning)\n",
    "  - [Intrinsic Motivation (Subgoal Achievement)](#intrinsic-motivation)\n",
    "  - [Hindsight Goal Transitions](#hindsight-goal-transitions)\n",
    "  - [Underlying Off-Policy Algorithm (e.g., DDPG/TD3/SAC adapted)](#underlying-off-policy-algorithm)\n",
    "- [Step-by-Step Explanation of HAC (2-Level Example)](#step-by-step-explanation-of-hac)\n",
    "- [Key Components of HAC](#key-components-of-hac)\n",
    "  - [Hierarchical Layers/Levels](#hierarchical-layerslevels)\n",
    "  - [Goal Representation](#goal-representation)\n",
    "  - [Goal-Conditioned Networks (Actor/Critic or Q-Network)](#goal-conditioned-networks)\n",
    "  - [Time Limit per Level ($H$)](#time-limit-per-level-h)\n",
    "  - [Subgoal Testing](#subgoal-testing)\n",
    "  - [Intrinsic Reward Mechanism](#intrinsic-reward-mechanism)\n",
    "  - [Hindsight Goal/Transition Strategy](#hindsight-goaltransition-strategy)\n",
    "  - [Replay Buffers (Per Level)](#replay-buffers-per-level)\n",
    "  - [Hyperparameters](#hyperparameters)\n",
    "- [Practical Example: Custom Multi-Goal Grid World](#practical-example-custom-multi-goal-grid-world)\n",
    "  - [Environment Design Rationale for HAC](#environment-design-rationale-for-hac)\n",
    "- [Setting up the Environment](#setting-up-the-environment)\n",
    "- [Creating the Custom Grid Environment](#creating-the-custom-grid-environment)\n",
    "- [Implementing the HAC Algorithm](#implementing-the-hac-algorithm)\n",
    "  - [Goal/State Representation](#goalstate-representation)\n",
    "  - [Defining the Low-Level Agent Network (Goal-Conditioned DQN-like)](#defining-the-low-level-agent-network)\n",
    "  - [Defining the High-Level Agent Network (Goal-Selecting DQN-like)](#defining-the-high-level-agent-network)\n",
    "  - [Defining the Hindsight Replay Buffer](#defining-the-hindsight-replay-buffer)\n",
    "  - [Soft Update Function](#soft-update-function)\n",
    "  - [The HAC Controller/Agent Structure](#the-hac-controlleragent-structure)\n",
    "    - [Low-Level Action Selection & Training](#low-level-action-selection--training)\n",
    "    - [High-Level Goal Selection & Training](#high-level-goal-selection--training)\n",
    "    - [Hindsight Transition Generation](#hindsight-transition-generation)\n",
    "- [Running the HAC Algorithm](#running-the-hac-algorithm)\n",
    "  - [Hyperparameter Setup](#hyperparameter-setup)\n",
    "  - [Initialization](#initialization)\n",
    "  - [Training Loop](#training-loop)\n",
    "- [Visualizing the Learning Process](#visualizing-the-learning-process)\n",
    "- [Analyzing the Learned Policies (Testing)](#analyzing-the-learned-policies-testing)\n",
    "- [Common Challenges and Extensions of HAC](#common-challenges-and-extensions-of-hac)\n",
    "- [Conclusion](#conclusion)\n",
    "\n",
    "## Introduction: Hierarchy in Reinforcement Learning\n",
    "\n",
    "Many real-world tasks exhibit a natural hierarchical structure. Solving complex problems often involves breaking them down into simpler sub-tasks or achieving intermediate goals. Hierarchical Reinforcement Learning (HRL) aims to leverage this structure by learning policies at multiple levels of temporal abstraction. Higher levels learn to set goals or sequences of sub-tasks, while lower levels learn to execute primitive actions to achieve those goals.\n",
    "\n",
    "## What is HAC?\n",
    "\n",
    "Hierarchical Actor-Critic (HAC), introduced by Levy et al. (2019), is a specific HRL algorithm that uses goal-conditioned policies operating at different time scales. It is designed to handle complex tasks with sparse rewards by allowing higher levels to set achievable subgoals for lower levels.\n",
    "\n",
    "### Core Idea: Levels of Abstraction & Goal Setting\n",
    "Imagine a multi-layered control system:\n",
    "- **High Level:** Observes the overall state and chooses a *subgoal* (e.g., a desired state configuration) to achieve within a relatively long time horizon.\n",
    "- **Low Level:** Receives the current state and the subgoal from the higher level. It then selects *primitive actions* to try and reach that subgoal within a shorter time limit.\n",
    "\n",
    "Each level acts like an RL agent, learning its own policy (actor) and value function (critic, or Q-function). The 'actions' for higher levels are the subgoals they set for the level below.\n",
    "\n",
    "### Key Mechanisms: Time Limits, Intrinsic Rewards, Hindsight\n",
    "HAC incorporates several key ideas:\n",
    "1.  **Fixed Time Limits ($H$):** Each level (except the lowest) gives the level below it a fixed number of primitive timesteps ($H$) to achieve the assigned subgoal.\n",
    "2.  **Goal-Conditioned Learning:** Policies and value functions at each level are conditioned not only on the state but also on the goal they are trying to achieve. $Q(state, action, goal)$ or $\\pi(action | state, goal)$.\n",
    "3.  **Intrinsic Motivation:** The lower level receives an intrinsic reward (e.g., +1 or 0) if it successfully reaches the subgoal assigned by the higher level within the time limit, and a penalty (e.g., -1) otherwise. This reward guides the lower level's learning.\n",
    "4.  **Hindsight Goal Transitions (HER-like):** To combat the sparsity of intrinsic rewards (reaching the *exact* subgoal might be rare initially), HAC uses hindsight. Even if the lower level failed to reach the *intended* goal, it reached *some* final state. HAC stores transitions in the replay buffer as if this *achieved* final state was the *intended* goal. This creates artificial success examples, dramatically improving sample efficiency for goal-conditioned learning.\n",
    "5.  **Off-Policy Learning:** Because of the hindsight relabeling, the underlying RL algorithm used at each level must be off-policy (like DDPG, TD3, SAC, or adapted DQN/DDQN) to learn from these modified transitions.\n",
    "\n",
    "## Why Hierarchical RL? Advantages\n",
    "\n",
    "- **Long Horizon Tasks:** HRL decomposes long, complex tasks into manageable sub-problems, making learning feasible where standard RL might fail due to sparse rewards or credit assignment issues.\n",
    "- **Structured Exploration:** Higher levels can guide exploration more effectively by setting diverse subgoals, rather than relying solely on random exploration in the primitive action space.\n",
    "- **Transfer and Reusability:** Lower-level policies trained to achieve specific subgoals might be reusable across different high-level tasks.\n",
    "- **Improved Sample Efficiency:** Hindsight mechanisms can significantly speed up learning in goal-conditioned tasks.\n",
    "\n",
    "## Where and How HAC is Used\n",
    "\n",
    "HAC and similar HRL approaches are applied to:\n",
    "1.  **Robotics:** Complex manipulation tasks (e.g., stacking blocks, opening doors), locomotion over obstacles.\n",
    "2.  **Navigation:** Long-range navigation in complex environments (e.g., mazes with keys and doors).\n",
    "3.  **Game Playing:** Strategy games requiring long-term planning.\n",
    "\n",
    "## Mathematical Foundation of HAC (Conceptual)\n",
    "\n",
    "Let's consider a 2-level hierarchy (Level 0: primitive actions, Level 1: sets subgoals for Level 0).\n",
    "\n",
    "### Multi-Level Policies & Value Functions\n",
    "- **Level 0 (Low):** Learns policy $\\pi_0(a_t | s_t, g_0)$ and value function $Q_0(s_t, a_t, g_0)$. It receives state $s_t$ and a subgoal $g_0$ (set by Level 1) and outputs primitive action $a_t$.\n",
    "- **Level 1 (High):** Learns policy $\\pi_1(g_0 | s_t, G)$ and value function $Q_1(s_t, g_0, G)$. It receives state $s_t$ and the overall task goal $G$ and outputs a subgoal $g_0$ for Level 0.\n",
    "\n",
    "### Goal-Conditioned Learning\n",
    "The inputs to the networks explicitly include the goal the level is trying to achieve. This is often done by concatenating the state representation with the goal representation.\n",
    "\n",
    "### Intrinsic Motivation (Subgoal Achievement)\n",
    "Level 0 is trained to maximize an intrinsic reward signal based on achieving $g_0$. A simple binary reward:\n",
    "$$ R_{intrinsic}(s_{t+H}, g_0) = \\begin{cases} 0 & \\text{if } \\text{test_goal}(s_{t+H}, g_0) \\text{ is True} \\\\ -1 & \\text{otherwise} \\end{cases} $$\n",
    "where `test_goal` checks if the state reached after $H$ steps satisfies the subgoal $g_0$ (e.g., distance below a threshold).\n",
    "\n",
    "### Hindsight Goal Transitions\n",
    "When storing a transition sequence $(s_t, ..., s_{t+H})$ taken by Level 0 aiming for goal $g_0$:\n",
    "1.  Store the original transition with the original goal $g_0$ and the calculated intrinsic reward $R_{intrinsic}(s_{t+H}, g_0)$.\n",
    "2.  Store an *additional* hindsight transition: Use the *achieved* state $s_{t+H}$ as the hindsight goal $g'_{0} = s_{t+H}$. The intrinsic reward for this transition is always the success reward (e.g., 0), as the achieved state *is* the goal by definition.\n",
    "This allows Level 0 to learn how to reach states it happened to land in, even if they weren't the original target.\n",
    "\n",
    "### Underlying Off-Policy Algorithm (e.g., DDPG/TD3/SAC adapted)\n",
    "Each level uses an off-policy algorithm to learn its goal-conditioned policy and/or value function from transitions (original and hindsight) stored in its replay buffer. For discrete actions (like our Grid World low level), an adapted DQN/DDQN can be used. For continuous actions/goals (original HAC), DDPG/TD3 is common.\n",
    "\n",
    "The update involves sampling a batch of goal-conditioned transitions $(s, a, r, s', g)$ and performing the corresponding off-policy update (e.g., Bellman error minimization for Q-learning/critic, policy gradient for actor).\n",
    "\n",
    "## Step-by-Step Explanation of HAC (2-Level Example)\n",
    "\n",
    "1.  **Initialize**: Networks $Q_0, Q'_0, Q_1, Q'_1$ (or Actor/Critics), replay buffers $D_0, D_1$. Hyperparameters $H$ (time limit), learning rates, etc.\n",
    "2.  **Loop for each episode**:\n",
    "    a.  Call Level 1 to select a subgoal $g_0$ for the overall goal $G$ based on initial state $s_0$. `subgoal = Level1.select_goal(s_0, G)`.\n",
    "    b.  **Loop (Low-Level Execution)** for $h = 1$ to $H$:\n",
    "        i.   Call Level 0 to select primitive action $a_t$ based on current state $s_t$ and subgoal $g_0$. `action = Level0.select_action(s_t, g_0)`.\n",
    "        ii.  Execute $a_t$, observe $r_t, s_{t+1}$.\n",
    "        iii. Store $(s_t, a_t, r_t, s_{t+1})$ temporarily for the current subgoal attempt.\n",
    "        iv.  If $s_{t+1}$ achieves subgoal $g_0$ (`test_goal(s_{t+1}, g_0)`), break low-level loop.\n",
    "        v.   If episode terminates based on environment `done` flag, handle episode end.\n",
    "        vi.  $s_t \\leftarrow s_{t+1}$.\n",
    "    c.  **End of Subgoal Attempt**: Let the sequence be $s_{start}, a_{start}, ..., s_{end}$.\n",
    "    d.  **Store Transitions with Hindsight**: \n",
    "        - For Level 0 buffer $D_0$: Store original transitions $(s_k, a_k, r_{intrinsic}, s_{k+1}, g_0)$ where $r_{intrinsic}$ depends on whether $s_{end}$ achieved $g_0$. Also store hindsight transitions $(s_k, a_k, 0, s_{k+1}, s_{end})$ (using $s_{end}$ as the achieved goal).\n",
    "        - For Level 1 buffer $D_1$: Store the high-level transition $(s_{start}, g_0, R_{high}, s_{end}, G)$ where $R_{high}$ is the sum of environment rewards during the subgoal attempt.\n",
    "    e.  **Perform Updates**: Sample batches from $D_0, D_1$ and update $Q_0, Q_1$ (and target networks) using the underlying off-policy algorithm.\n",
    "    f.  If the overall goal $G$ is achieved, or max episode steps reached, end episode.\n",
    "3.  **Repeat**.\n",
    "\n",
    "## Key Components of HAC\n",
    "\n",
    "### Hierarchical Layers/Levels\n",
    "- Multiple policies operating at different temporal abstractions.\n",
    "\n",
    "### Goal Representation\n",
    "- How subgoals are defined (e.g., target state, change in state).\n",
    "\n",
    "### Goal-Conditioned Networks\n",
    "- Actors/Critics/Q-Networks take state *and* goal as input.\n",
    "\n",
    "### Time Limit per Level ($H$)\n",
    "- Maximum steps allocated for a lower level to achieve a subgoal.\n",
    "\n",
    "### Subgoal Testing\n",
    "- Function to determine if a state satisfies a given subgoal.\n",
    "\n",
    "### Intrinsic Reward Mechanism\n",
    "- How lower levels are rewarded for achieving assigned subgoals.\n",
    "\n",
    "### Hindsight Goal/Transition Strategy\n",
    "- Relabeling transitions with achieved goals to improve learning efficiency.\n",
    "\n",
    "### Replay Buffers (Per Level)\n",
    "- Store goal-conditioned transitions, including original and hindsight goals.\n",
    "\n",
    "### Hyperparameters\n",
    "- Number of levels, time limits $H$ per level.\n",
    "- Underlying RL algorithm hyperparameters (learning rates, $\\tau$, $\\gamma$, buffer/batch sizes).\n",
    "- Subgoal testing tolerance.\n",
    "- Hindsight strategy parameters.\n",
    "\n",
    "## Practical Example: Custom Multi-Goal Grid World\n",
    "\n",
    "### Environment Design Rationale for HAC\n",
    "Using the Grid World allows us to clearly define:\n",
    "- **Goals:** Target `(row, col)` tuples.\n",
    "- **Goal Testing:** Exact match `state == goal`.\n",
    "- **State/Goal Concatenation:** Easily combine state `(r, c)` and goal `(gr, gc)` as input `(r, c, gr, gc)` to networks.\n",
    "- **Underlying Algorithm:** Adapt DQN/DDQN for the discrete actions at the lower level and for goal selection at the higher level.\n",
    "\n",
    "**Environment:** Standard 10x10 grid world. Final goal $G=(9,9)$.\n",
    "- **Level 1 (High):** Observes state `(r, c)`, selects subgoal $g_0=(gr_0, gc_0)$ from possible grid cells. Aims to reach final goal $G$. Receives environment reward.\n",
    "- **Level 0 (Low):** Observes state `(r, c)`, receives subgoal $g_0=(gr_0, gc_0)$. Selects primitive actions (U,D,L,R) to reach $g_0$ within $H$ steps. Receives intrinsic reward (-1 or 0).\n",
    "\n",
    "**Note:** This is a simplified HAC structure. The original paper used DDPG as the base and continuous states/actions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setting up the Environment\n",
    "\n",
    "Import libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: cpu\n"
     ]
    }
   ],
   "source": [
    "# Import necessary libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "import math\n",
    "from collections import namedtuple, deque, defaultdict\n",
    "from itertools import count\n",
    "from typing import List, Tuple, Dict, Optional, Callable, Any, Union\n",
    "import copy\n",
    "\n",
    "# Import PyTorch\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F\n",
    "\n",
    "# Set up device\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(f\"Using device: {device}\")\n",
    "\n",
    "# Set random seeds\n",
    "seed = 42\n",
    "random.seed(seed)\n",
    "np.random.seed(seed)\n",
    "torch.manual_seed(seed)\n",
    "if torch.cuda.is_available():\n",
    "    torch.cuda.manual_seed_all(seed)\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating the Custom Grid Environment\n",
    "\n",
    "Using the tabular version, suitable for discrete states/goals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class GridEnvironmentTabular:\n",
    "    \"\"\"\n",
    "    Grid World returning state tuples, suitable for tabular methods.\n",
    "    Rewards: +10 (goal), -1 (wall), -0.1 (step).\n",
    "    \"\"\"\n",
    "    def __init__(self, rows: int = 10, cols: int = 10) -> None:\n",
    "        self.rows: int = rows\n",
    "        self.cols: int = cols\n",
    "        self.start_state: Tuple[int, int] = (0, 0)\n",
    "        self.goal_state: Tuple[int, int] = (rows - 1, cols - 1)\n",
    "        self.state: Tuple[int, int] = self.start_state\n",
    "        self.action_dim: int = 4\n",
    "        self.action_map: Dict[int, Tuple[int, int]] = {0: (-1, 0), 1: (1, 0), 2: (0, -1), 3: (0, 1)}\n",
    "\n",
    "    def reset(self) -> Tuple[int, int]:\n",
    "        \"\"\" Resets environment, returns state tuple. \"\"\"\n",
    "        self.state = self.start_state\n",
    "        return self.state\n",
    "\n",
    "    def step(self, action: int) -> Tuple[Tuple[int, int], float, bool]:\n",
    "        \"\"\"\n",
    "        Performs one step, returns (next_state_tuple, reward, done).\n",
    "        \"\"\"\n",
    "        if self.state == self.goal_state:\n",
    "            return self.state, 0.0, True\n",
    "        \n",
    "        dr, dc = self.action_map[action]\n",
    "        current_row, current_col = self.state\n",
    "        next_row, next_col = current_row + dr, current_col + dc\n",
    "        \n",
    "        reward: float = -0.1\n",
    "        if not (0 <= next_row < self.rows and 0 <= next_col < self.cols):\n",
    "            next_row, next_col = current_row, current_col # Stay in place\n",
    "            reward = -1.0\n",
    "            \n",
    "        self.state = (next_row, next_col)\n",
    "        next_state_tuple = self.state\n",
    "        \n",
    "        done: bool = (self.state == self.goal_state)\n",
    "        if done:\n",
    "            reward = 10.0\n",
    "            \n",
    "        return next_state_tuple, reward, done\n",
    "\n",
    "    def get_action_space_size(self) -> int:\n",
    "        return self.action_dim\n",
    "    \n",
    "    def get_state(self) -> Tuple[int, int]:\n",
    "        \"\"\" Returns the current state. \"\"\"\n",
    "        return self.state\n",
    "    \n",
    "    # Add this method to GridEnvironmentTabular class\n",
    "    def is_terminal(self, state: Tuple[int, int]) -> bool:\n",
    "        \"\"\" Checks if the state is terminal (goal state). \"\"\"\n",
    "        return state == self.goal_state\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class GridEnvironmentHAC(GridEnvironmentTabular):\n",
    "    \"\"\" Grid world adapted slightly for HAC state/goal use. \"\"\"\n",
    "    def __init__(self, rows: int = 10, cols: int = 10):\n",
    "        super().__init__(rows=rows, cols=cols)\n",
    "        # State representation for networks will be normalized\n",
    "        self.state_dim = 2 # (r, c)\n",
    "        self.goal_dim = 2 # (gr, gc)\n",
    "        self.max_coord_float = float(max(rows - 1, cols - 1, 1)) # For normalization\n",
    "\n",
    "    def _normalize_state_goal(self, state_or_goal: Tuple[int, int]) -> np.ndarray:\n",
    "        \"\"\" Normalizes a state or goal tuple to range [0, 1]. \"\"\"\n",
    "        return np.array(state_or_goal, dtype=np.float32) / self.max_coord_float\n",
    "\n",
    "    def get_normalized_state(self) -> np.ndarray:\n",
    "        \"\"\" Returns the current state, normalized. \"\"\"\n",
    "        return self._normalize_state_goal(self.get_state())\n",
    "    \n",
    "    # Inherits reset(), step(), is_terminal(), get_possible_actions(), etc.\n",
    "    # MCTS is not needed, HAC uses learned policies/Q-functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instantiate the environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HAC Grid Environment: 10x10\n",
      "State Dim: 2, Goal Dim: 2, Action Dim: 4\n",
      "Final Goal (raw): (9, 9), Final Goal (norm): [1. 1.]\n"
     ]
    }
   ],
   "source": [
    "hac_env = GridEnvironmentHAC(rows=10, cols=10)\n",
    "state_dim_hac = hac_env.state_dim\n",
    "goal_dim_hac = hac_env.goal_dim \n",
    "action_dim_hac = hac_env.get_action_space_size()\n",
    "final_goal_state = hac_env.goal_state # The overall task goal\n",
    "final_goal_norm = hac_env._normalize_state_goal(final_goal_state)\n",
    "\n",
    "print(f\"HAC Grid Environment: {hac_env.rows}x{hac_env.cols}\")\n",
    "print(f\"State Dim: {state_dim_hac}, Goal Dim: {goal_dim_hac}, Action Dim: {action_dim_hac}\")\n",
    "print(f\"Final Goal (raw): {final_goal_state}, Final Goal (norm): {final_goal_norm}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Implementing the HAC Algorithm\n",
    "\n",
    "We need goal-conditioned networks, a hindsight replay buffer, and the hierarchical control logic.\n",
    "\n",
    "### Goal/State Representation\n",
    "Networks will take concatenated `(state, goal)` as input. We'll use normalized coordinates."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining the Low-Level Agent Network (Goal-Conditioned DQN-like)\n",
    "\n",
    "Takes `(state, goal)` and outputs Q-values for primitive actions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class GoalConditionedQNetwork(nn.Module):\n",
    "    \"\"\" Outputs Q(state, action | goal). Used for both levels. \"\"\"\n",
    "    def __init__(self, input_dim: int, action_dim: int):\n",
    "        # input_dim = state_dim + goal_dim \n",
    "        super(GoalConditionedQNetwork, self).__init__()\n",
    "        self.fc1 = nn.Linear(input_dim, 128)\n",
    "        self.fc2 = nn.Linear(128, 128)\n",
    "        self.fc3 = nn.Linear(128, action_dim) # Q-value per action\n",
    "\n",
    "    def forward(self, state_goal_concat: torch.Tensor) -> torch.Tensor:\n",
    "        \"\"\" Maps concatenated (state, goal) to Q-values. \"\"\"\n",
    "        x = F.relu(self.fc1(state_goal_concat))\n",
    "        x = F.relu(self.fc2(x))\n",
    "        q_values = self.fc3(x)\n",
    "        return q_values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining the High-Level Agent Network (Goal-Selecting DQN-like)\n",
    "\n",
    "For simplicity, we treat the high level's \"action\" (selecting a subgoal) as choosing from a discrete set of possible subgoals (e.g., all possible grid cells). This allows using a similar Q-network structure. A more complex approach for continuous goals would use DDPG-like actor/critic. \n",
    "\n",
    "*Note: This discretization of the high-level action space is a simplification.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "High level has 100 possible actions (goals).\n"
     ]
    }
   ],
   "source": [
    "# The high level selects a goal (state tuple) as its action.\n",
    "# We need a mapping from action index to goal state tuple.\n",
    "all_possible_goals = [(r, c) for r in range(hac_env.rows) for c in range(hac_env.cols)]\n",
    "num_possible_goals = len(all_possible_goals)\n",
    "action_to_goal_map = {i: goal for i, goal in enumerate(all_possible_goals)}\n",
    "goal_to_action_map = {goal: i for i, goal in enumerate(all_possible_goals)}\n",
    "\n",
    "print(f\"High level has {num_possible_goals} possible actions (goals).\")\n",
    "\n",
    "# High level uses the same Q-network structure, but output size is num_possible_goals\n",
    "# Input dim = state_dim + final_goal_dim (which is also state_dim)\n",
    "high_level_input_dim = state_dim_hac + goal_dim_hac\n",
    "high_level_action_dim = num_possible_goals\n",
    "\n",
    "HighLevelQNetwork = GoalConditionedQNetwork # Reuse the class structure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining the Hindsight Replay Buffer\n",
    "\n",
    "Stores goal-conditioned transitions and handles hindsight relabeling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define transition structure for HAC buffer\n",
    "HACTransition = namedtuple('HACTransition', \n",
    "                           ('state', 'action', 'reward', 'next_state', \n",
    "                            'goal', 'done', 'level')) # Add goal and level\n",
    "\n",
    "class HindsightReplayBuffer:\n",
    "    def __init__(self, capacity: int, hindsight_prob: float = 0.8):\n",
    "        self.memory = deque([], maxlen=capacity)\n",
    "        self.hindsight_prob = hindsight_prob # Probability of using hindsight goal\n",
    "\n",
    "    def push(self, state: np.ndarray, action: Union[int, np.ndarray], reward: float, \n",
    "               next_state: np.ndarray, goal: np.ndarray, done: bool, level: int,\n",
    "               achieved_goal: Optional[np.ndarray] = None) -> None:\n",
    "        \"\"\" Saves a transition, potentially storing achieved goal for hindsight. \"\"\"\n",
    "        # Store data as numpy arrays for consistency\n",
    "        self.memory.append({\n",
    "            'state': state,\n",
    "            'action': np.array([action]) if isinstance(action, int) else action,\n",
    "            'reward': reward,\n",
    "            'next_state': next_state,\n",
    "            'goal': goal,\n",
    "            'done': done,\n",
    "            'level': level,\n",
    "            'achieved_goal': achieved_goal if achieved_goal is not None else next_state # Store achieved goal\n",
    "        })\n",
    "\n",
    "    def sample(self, batch_size: int, level: int) -> Optional[Tuple[torch.Tensor, ...]]:\n",
    "        \"\"\" Samples a batch, applying hindsight relabeling. \"\"\"\n",
    "        # Filter memory for the correct level\n",
    "        level_memory = [t for t in self.memory if t['level'] == level]\n",
    "        if len(level_memory) < batch_size:\n",
    "            return None\n",
    "            \n",
    "        batch_indices = np.random.choice(len(level_memory), batch_size, replace=False)\n",
    "        batch = [level_memory[i] for i in batch_indices]\n",
    "\n",
    "        # Apply hindsight relabeling\n",
    "        states, actions, rewards, next_states, goals, dones = [], [], [], [], [], []\n",
    "        for transition in batch:\n",
    "            state = transition['state']\n",
    "            action = transition['action']\n",
    "            reward = transition['reward']\n",
    "            next_state = transition['next_state']\n",
    "            original_goal = transition['goal']\n",
    "            done = transition['done']\n",
    "            achieved = transition['achieved_goal']\n",
    "            \n",
    "            goal_to_use = original_goal\n",
    "            # Hindsight: With probability p, replace original goal with achieved goal\n",
    "            if random.random() < self.hindsight_prob:\n",
    "                goal_to_use = achieved\n",
    "                # If goal was achieved, reward is success_reward (e.g., 0 for low level)\n",
    "                # and done should reflect goal achievement for this hindsight sample.\n",
    "                # We need a goal testing function here.\n",
    "                if level == 0: # Assuming Level 0 specific intrinsic reward\n",
    "                    # For grid world, goal is achieved if next_state == goal_to_use\n",
    "                    dist_sq = np.sum((next_state - goal_to_use)**2)\n",
    "                    if dist_sq < 1e-6: # Check if achieved goal is the next state\n",
    "                        reward = 0.0 # Success intrinsic reward\n",
    "                        done = True # Treat as done for this hindsight transition\n",
    "                    else:\n",
    "                        reward = -1.0 # Failure intrinsic reward for hindsight\n",
    "                        # done remains False unless original was True\n",
    "                        done = transition['done'] # Use original done if goal wasn't the achieved state\n",
    "                # High level hindsight is simpler: just use env reward \n",
    "                # and original done flag, but learn V(state, achieved_goal).\n",
    "                elif level == 1:\n",
    "                     reward = transition['reward'] # Keep original env reward\n",
    "                     done = transition['done']\n",
    "            else:\n",
    "                # Use original goal and reward\n",
    "                goal_to_use = original_goal\n",
    "                reward = transition['reward']\n",
    "                done = transition['done']\n",
    "\n",
    "            states.append(state)\n",
    "            actions.append(action)\n",
    "            rewards.append(reward)\n",
    "            next_states.append(next_state)\n",
    "            goals.append(goal_to_use)\n",
    "            dones.append(done)\n",
    "\n",
    "        # Convert to tensors\n",
    "        states_t = torch.from_numpy(np.array(states)).float().to(device)\n",
    "        actions_t = torch.from_numpy(np.array(actions)).long().to(device) # Discrete actions\n",
    "        rewards_t = torch.tensor(rewards, dtype=torch.float32).unsqueeze(1).to(device)\n",
    "        next_states_t = torch.from_numpy(np.array(next_states)).float().to(device)\n",
    "        goals_t = torch.from_numpy(np.array(goals)).float().to(device)\n",
    "        dones_t = torch.tensor(dones, dtype=torch.float32).unsqueeze(1).to(device)\n",
    "\n",
    "        return states_t, actions_t, rewards_t, next_states_t, goals_t, dones_t\n",
    "\n",
    "    def __len__(self) -> int:\n",
    "        return len(self.memory)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Soft Update Function\n",
    "\n",
    "Standard soft update."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def soft_update(target_net: nn.Module, main_net: nn.Module, tau: float) -> None:\n",
    "    for target_param, main_param in zip(target_net.parameters(), main_net.parameters()):\n",
    "        target_param.data.copy_(tau * main_param.data + (1.0 - tau) * target_param.data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The HAC Controller/Agent Structure\n",
    "\n",
    "We need a structure to manage the levels, interactions, and updates. A class `HACAgent` can encapsulate this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class HACAgent:\n",
    "    def __init__(self,\n",
    "                 env: GridEnvironmentHAC,\n",
    "                 num_levels: int,\n",
    "                 H: int,  # Time limit per level\n",
    "                 k_hindsight: int,  # Hindsight transitions per original\n",
    "                 buffer_size: int,\n",
    "                 batch_size: int,\n",
    "                 gamma: float,\n",
    "                 tau: float,\n",
    "                 actor_lr: float,  # Using Q-networks, so this is Q-net LR\n",
    "                 epsilon_start: float,\n",
    "                 epsilon_end: float,\n",
    "                 epsilon_decay_steps: int,\n",
    "                 device: torch.device):\n",
    "\n",
    "        if num_levels != 2:\n",
    "            raise NotImplementedError(\"Only 2 levels implemented\")\n",
    "\n",
    "        self.env = env\n",
    "        self.num_levels = num_levels\n",
    "        self.H = H  # Max steps for low level to achieve high-level goal\n",
    "        self.k = k_hindsight\n",
    "        self.buffer_size = buffer_size\n",
    "        self.batch_size = batch_size\n",
    "        self.gamma = gamma\n",
    "        self.tau = tau\n",
    "        self.lr = actor_lr\n",
    "        self.epsilon_start = epsilon_start\n",
    "        self.epsilon_end = epsilon_end\n",
    "        self.epsilon_decay_steps = epsilon_decay_steps\n",
    "        self.device = device\n",
    "\n",
    "        self.state_dim = env.state_dim\n",
    "        self.goal_dim = env.goal_dim\n",
    "        self.primitive_action_dim = env.get_action_space_size()\n",
    "        self.subgoal_action_dim = env.rows * env.cols  # High level action = choosing a cell\n",
    "        self.final_goal_raw = env.goal_state\n",
    "        self.final_goal_norm = env._normalize_state_goal(self.final_goal_raw)\n",
    "        self.final_goal_tensor = torch.from_numpy(self.final_goal_norm).float().to(device)\n",
    "\n",
    "        # --- Level 0 (Low Level) Components ---\n",
    "        self.low_level_qnet = GoalConditionedQNetwork(self.state_dim + self.goal_dim, self.primitive_action_dim).to(\n",
    "            device)\n",
    "        self.low_level_target_qnet = GoalConditionedQNetwork(self.state_dim + self.goal_dim,\n",
    "                                                              self.primitive_action_dim).to(device)\n",
    "        self.low_level_target_qnet.load_state_dict(self.low_level_qnet.state_dict())\n",
    "        self.low_level_optimizer = optim.Adam(self.low_level_qnet.parameters(), lr=self.lr)\n",
    "        self.low_level_buffer = HindsightReplayBuffer(buffer_size, hindsight_prob=0.8)\n",
    "\n",
    "        # --- Level 1 (High Level) Components ---\n",
    "        self.high_level_qnet = GoalConditionedQNetwork(self.state_dim + self.goal_dim, self.subgoal_action_dim).to(\n",
    "            device)\n",
    "        self.high_level_target_qnet = GoalConditionedQNetwork(self.state_dim + self.goal_dim,\n",
    "                                                               self.subgoal_action_dim).to(device)\n",
    "        self.high_level_target_qnet.load_state_dict(self.high_level_qnet.state_dict())\n",
    "        self.high_level_optimizer = optim.Adam(self.high_level_qnet.parameters(), lr=self.lr)\n",
    "        self.high_level_buffer = HindsightReplayBuffer(buffer_size // H, hindsight_prob=0.8)  # High level gets less data\n",
    "\n",
    "        self.total_steps = 0\n",
    "        self.current_epsilon = self.epsilon_start\n",
    "\n",
    "    def _test_goal(self, state: np.ndarray, goal: np.ndarray, threshold: float = 1e-3) -> bool:\n",
    "        \"\"\" Check if state achieves goal (using normalized L2 distance). \"\"\"\n",
    "        # For grid world, check exact match of raw coordinates for simplicity\n",
    "        state_raw = tuple(np.round(state * self.env.max_coord_float).astype(int))\n",
    "        goal_raw = tuple(np.round(goal * self.env.max_coord_float).astype(int))\n",
    "        return state_raw == goal_raw\n",
    "        # Alternative (L2 distance for potentially continuous states):\n",
    "        # dist_sq = np.sum((state - goal)**2)\n",
    "        # return dist_sq < threshold**2\n",
    "\n",
    "    def _get_intrinsic_reward(self, state: np.ndarray, goal: np.ndarray) -> float:\n",
    "        \"\"\" Binary intrinsic reward for low level. \"\"\"\n",
    "        return 0.0 if self._test_goal(state, goal) else -1.0\n",
    "\n",
    "    def select_action(self, level: int, state_norm: np.ndarray, goal_norm: np.ndarray) -> Union[int, np.ndarray]:\n",
    "        \"\"\" Select action (primitive or subgoal) using epsilon-greedy. \"\"\"\n",
    "        state_tensor = torch.from_numpy(state_norm).float().to(self.device)\n",
    "        goal_tensor = torch.from_numpy(goal_norm).float().to(self.device)\n",
    "        state_goal_concat = torch.cat([state_tensor, goal_tensor]).unsqueeze(0)  # Add batch dim\n",
    "\n",
    "        if level == 0:\n",
    "            qnet = self.low_level_qnet\n",
    "            action_dim = self.primitive_action_dim\n",
    "        else:  # Level 1\n",
    "            qnet = self.high_level_qnet\n",
    "            action_dim = self.subgoal_action_dim\n",
    "\n",
    "        qnet.eval()\n",
    "        with torch.no_grad():\n",
    "            q_values = qnet(state_goal_concat).squeeze()\n",
    "        qnet.train()\n",
    "\n",
    "        if random.random() < self.current_epsilon:\n",
    "            action_idx = random.randrange(action_dim)\n",
    "        else:\n",
    "            action_idx = q_values.argmax().item()\n",
    "\n",
    "        # If high level, convert action index back to goal state\n",
    "        if level == 1:\n",
    "            goal_tuple = action_to_goal_map[action_idx]\n",
    "            action = self.env._normalize_state_goal(goal_tuple)  # Return normalized goal\n",
    "        else:\n",
    "            action = action_idx  # Return primitive action index\n",
    "\n",
    "        return action\n",
    "\n",
    "    def run_level(self, level: int, initial_state_norm: np.ndarray, goal_norm: np.ndarray) -> Tuple[\n",
    "        List[Dict], float, bool, np.ndarray]:\n",
    "        \"\"\"\n",
    "        Run one level of the hierarchy.\n",
    "        Returns: list of transitions, cumulative env reward, goal achieved flag, final state.\n",
    "        \"\"\"\n",
    "        state_norm = initial_state_norm\n",
    "        cumulative_env_reward = 0.0\n",
    "        transitions = []\n",
    "        goal_achieved = False\n",
    "\n",
    "        max_steps = self.H  # Time limit for this level\n",
    "\n",
    "        for t in range(max_steps):\n",
    "            # Select action (primitive or subgoal) based on current level policy\n",
    "            action = self.select_action(level, state_norm, goal_norm)\n",
    "\n",
    "            if level == 0:  # Low level executes primitive action\n",
    "                action_primitive = action\n",
    "                current_state_raw = self.env.get_state()  # Get raw state before step\n",
    "                next_state_raw, env_reward, env_done = self.env.step(action_primitive)\n",
    "                next_state_norm = self.env.get_normalized_state()\n",
    "                final_state_norm_in_traj = next_state_norm  # Achieved goal is the final state\n",
    "\n",
    "                # Calculate intrinsic reward based on the assigned goal\n",
    "                intrinsic_reward = self._get_intrinsic_reward(next_state_norm, goal_norm)\n",
    "\n",
    "                # Store low-level transition (uses intrinsic reward)\n",
    "                transitions.append({\n",
    "                    'state': state_norm,\n",
    "                    'action': action_primitive,\n",
    "                    'reward': intrinsic_reward,\n",
    "                    'next_state': next_state_norm,\n",
    "                    'goal': goal_norm,\n",
    "                    'done': env_done or self._test_goal(next_state_norm, goal_norm),\n",
    "                    'level': 0,\n",
    "                    'achieved_goal': next_state_norm\n",
    "                })\n",
    "\n",
    "                cumulative_env_reward += env_reward\n",
    "                state_norm = next_state_norm\n",
    "                self.total_steps += 1  # Count primitive steps\n",
    "\n",
    "                # Check if assigned goal or final goal achieved\n",
    "                if self._test_goal(state_norm, goal_norm):\n",
    "                    goal_achieved = True\n",
    "                    break\n",
    "                if env_done:  # Check if env terminated\n",
    "                    break\n",
    "\n",
    "            else:  # High level executes by calling lower level\n",
    "                subgoal_norm = action  # High level action is the subgoal\n",
    "\n",
    "                # Run level 0 to achieve subgoal_norm\n",
    "                low_level_transitions, low_level_env_reward, low_level_goal_achieved, final_state_norm_in_traj = \\\n",
    "                    self.run_level(level=0, initial_state_norm=state_norm, goal_norm=subgoal_norm)\n",
    "\n",
    "                cumulative_env_reward += low_level_env_reward\n",
    "\n",
    "                # Store high-level transition (uses environment reward)\n",
    "                transitions.append({\n",
    "                    'state': initial_state_norm,  # State when goal was chosen\n",
    "                    'action': subgoal_norm,  # Action was the chosen subgoal\n",
    "                    'reward': low_level_env_reward,  # Reward is sum of env rewards\n",
    "                    'next_state': final_state_norm_in_traj,  # Next state is where low level ended up\n",
    "                    'goal': goal_norm,  # The goal the high level was trying to achieve (final goal)\n",
    "                    'done': self.env.is_terminal(self.env.get_state()),  # Overall done flag\n",
    "                    'level': 1,\n",
    "                    'achieved_goal': final_state_norm_in_traj  # Achieved goal is where low level ended\n",
    "                })\n",
    "\n",
    "                state_norm = final_state_norm_in_traj\n",
    "\n",
    "                # Check if overall goal achieved\n",
    "                if self._test_goal(state_norm, self.final_goal_norm):\n",
    "                    goal_achieved = True  # High level goal achieved\n",
    "                    break\n",
    "                if self.env.is_terminal(self.env.get_state()):  # Or env terminated\n",
    "                    break\n",
    "\n",
    "        # After H steps or goal achievement/termination\n",
    "        return transitions, cumulative_env_reward, goal_achieved, state_norm\n",
    "\n",
    "    def store_and_update(self, transitions: List[Dict]) -> None:\n",
    "        \"\"\" Store transitions with hindsight and perform updates. \"\"\"\n",
    "        # Store original and hindsight transitions\n",
    "        for trans in transitions:\n",
    "            level = trans['level']\n",
    "            buffer = self.low_level_buffer if level == 0 else self.high_level_buffer\n",
    "\n",
    "            # Store original transition\n",
    "            buffer.push(**trans)\n",
    "\n",
    "            # Store k hindsight transitions\n",
    "            # In basic HAC, only the final achieved state is used as hindsight goal.\n",
    "            # We push it during the sample phase for simplicity here.\n",
    "            # More advanced HER samples k goals from the achieved trajectory.\n",
    "            # For now, the sampling method handles the hindsight probability.\n",
    "\n",
    "        # Perform updates if buffer is large enough\n",
    "        if len(self.low_level_buffer) > self.batch_size:\n",
    "            l0_loss, _ = self.update_level(level=0)  # Capture returned loss\n",
    "        else:\n",
    "            l0_loss = 0.0  # No update, loss is zero\n",
    "        if len(self.high_level_buffer) > self.batch_size:\n",
    "            l1_loss, _ = self.update_level(level=1) # Capture returned loss\n",
    "        else:\n",
    "             l1_loss = 0.0 # No update, loss is zero\n",
    "\n",
    "        return l0_loss, l1_loss # Return the losses\n",
    "\n",
    "    def update_level(self, level: int) -> Tuple[float, float]:\n",
    "        \"\"\" Performs DQN-like update for a specific level. \"\"\"\n",
    "        if level == 0:\n",
    "            qnet = self.low_level_qnet\n",
    "            target_qnet = self.low_level_target_qnet\n",
    "            optimizer = self.low_level_optimizer\n",
    "            buffer = self.low_level_buffer\n",
    "            action_dim = self.primitive_action_dim\n",
    "            is_high_level_action = False\n",
    "        else:\n",
    "            qnet = self.high_level_qnet\n",
    "            target_qnet = self.high_level_target_qnet\n",
    "            optimizer = self.high_level_optimizer\n",
    "            buffer = self.high_level_buffer\n",
    "            action_dim = self.subgoal_action_dim\n",
    "            is_high_level_action = True\n",
    "\n",
    "        if len(buffer) < self.batch_size:\n",
    "            return 0.0, 0.0  # Not enough samples\n",
    "\n",
    "        # Sample batch with hindsight relabeling\n",
    "        sample = buffer.sample(self.batch_size, level)\n",
    "        if sample is None:\n",
    "            return 0.0, 0.0\n",
    "\n",
    "        states_t, actions_t, rewards_t, next_states_t, goals_t, dones_t = sample\n",
    "\n",
    "        # Concatenate state and goal for network input\n",
    "        state_goal_input = torch.cat([states_t, goals_t], dim=1)\n",
    "        next_state_goal_input = torch.cat([next_states_t, goals_t], dim=1)\n",
    "\n",
    "        # --- Critic (Q-Network) Update ---\n",
    "        # Get current Q values for actions taken\n",
    "        current_q_all_actions = qnet(state_goal_input)\n",
    "\n",
    "        # If high level, actions_t are goal states represented as *normalized vectors*, need conversion to indices\n",
    "        if is_high_level_action:\n",
    "            # Find the index in `action_to_goal_map` that corresponds to the *closest* normalized goal\n",
    "            # This part requires an understanding of torch operations to efficiently compute distances\n",
    "\n",
    "            # Convert to numpy for distance computation\n",
    "            actions_np = actions_t.cpu().numpy()\n",
    "\n",
    "            action_indices = []\n",
    "            for i in range(actions_np.shape[0]):  # Iterate over batch\n",
    "                goal_vec = actions_np[i]\n",
    "                min_dist = float('inf')\n",
    "                best_index = -1\n",
    "                for index, potential_goal_vec in action_to_goal_map.items():\n",
    "                    potential_goal_vec = hac_env._normalize_state_goal(potential_goal_vec)  # normalize the potential goal vector\n",
    "                    dist = np.linalg.norm(goal_vec - potential_goal_vec)\n",
    "                    if dist < min_dist:\n",
    "                        min_dist = dist\n",
    "                        best_index = index\n",
    "                action_indices.append(best_index)\n",
    "            action_indices_t = torch.tensor(action_indices).long().to(device)  # Convert to tensor\n",
    "\n",
    "        else:\n",
    "            action_indices_t = actions_t.squeeze(1)  # Actions are already indices\n",
    "\n",
    "        current_q = current_q_all_actions.gather(1, action_indices_t.unsqueeze(1))\n",
    "\n",
    "        # Get target Q values using target network (Double DQN style)\n",
    "        with torch.no_grad():\n",
    "            # Select best action for next state using *main* network\n",
    "            next_q_main = qnet(next_state_goal_input)\n",
    "            best_next_action = next_q_main.argmax(dim=1, keepdim=True)\n",
    "            # Evaluate this best action using *target* network\n",
    "            next_q_target = target_qnet(next_state_goal_input)\n",
    "            max_next_q = torch.gather(next_q_target, 1, best_next_action)\n",
    "            # Compute TD target\n",
    "            td_target = rewards_t + self.gamma * (1.0 - dones_t) * max_next_q\n",
    "\n",
    "        # Calculate loss\n",
    "        loss = F.mse_loss(current_q, td_target)\n",
    "\n",
    "        # Optimize\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        # Soft update target network\n",
    "        soft_update(target_qnet, qnet, self.tau)\n",
    "\n",
    "        return loss.item(), 0.0  # Return critic loss, dummy actor loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Running the HAC Algorithm\n",
    "\n",
    "### Hyperparameter Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hyperparameters for HAC on Custom Grid World\n",
    "BUFFER_SIZE_HAC = int(1e5)\n",
    "BATCH_SIZE_HAC = 128\n",
    "GAMMA_HAC = 0.98 # Discount factor (might be lower for HRL)\n",
    "TAU_HAC = 1e-3   # Soft update factor\n",
    "LR_HAC = 1e-4    # Learning rate for Q-networks\n",
    "EPSILON_HAC_START = 1.0\n",
    "EPSILON_HAC_END = 0.05\n",
    "EPSILON_HAC_DECAY_STEPS = 200000 # Decay over primitive steps\n",
    "H_TIMESACLE = 10 # Time limit for low level (max steps)\n",
    "K_HINDSIGHT = 4  # Not directly used in this simple HER sampling, implicitly p=0.8\n",
    "HINDSIGHT_PROB = 0.8\n",
    "\n",
    "NUM_EPISODES_HAC = 500\n",
    "MAX_TOTAL_STEPS_EPISODE = 100 # Max primitive steps per *overall* episode"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize Environment\n",
    "hac_env_run = GridEnvironmentHAC(rows=10, cols=10)\n",
    "\n",
    "# Initialize HAC Agent\n",
    "hac_agent = HACAgent(\n",
    "    env=hac_env_run,\n",
    "    num_levels=2,\n",
    "    H=H_TIMESACLE,\n",
    "    k_hindsight=K_HINDSIGHT,\n",
    "    buffer_size=BUFFER_SIZE_HAC,\n",
    "    batch_size=BATCH_SIZE_HAC,\n",
    "    gamma=GAMMA_HAC,\n",
    "    tau=TAU_HAC,\n",
    "    actor_lr=LR_HAC,\n",
    "    epsilon_start=EPSILON_HAC_START,\n",
    "    epsilon_end=EPSILON_HAC_END,\n",
    "    epsilon_decay_steps=EPSILON_HAC_DECAY_STEPS,\n",
    "    device=device\n",
    ")\n",
    "\n",
    "# Lists for plotting\n",
    "hac_episode_rewards = [] # Store *environment* rewards\n",
    "hac_episode_lengths = []\n",
    "hac_episode_epsilons = []\n",
    "hac_low_level_losses = []\n",
    "hac_high_level_losses = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting HAC Training...\n",
      "Ep 20/500 | Avg Reward: -17.16 | Avg Length: 95.3 | L0_Loss: 0.0000 | L1_Loss: 1.6512 | Eps: 0.964\n",
      "Ep 40/500 | Avg Reward: -19.36 | Avg Length: 93.9 | L0_Loss: 0.0000 | L1_Loss: 5.1324 | Eps: 0.926\n",
      "Ep 60/500 | Avg Reward: -18.57 | Avg Length: 93.2 | L0_Loss: 0.0000 | L1_Loss: 4.8221 | Eps: 0.888\n",
      "Ep 80/500 | Avg Reward: -21.87 | Avg Length: 95.5 | L0_Loss: 0.0000 | L1_Loss: 4.7927 | Eps: 0.850\n",
      "Ep 100/500 | Avg Reward: -22.32 | Avg Length: 95.6 | L0_Loss: 0.0000 | L1_Loss: 4.8101 | Eps: 0.812\n",
      "Ep 120/500 | Avg Reward: -21.01 | Avg Length: 90.5 | L0_Loss: 0.0000 | L1_Loss: 4.9521 | Eps: 0.774\n",
      "Ep 140/500 | Avg Reward: -27.57 | Avg Length: 96.0 | L0_Loss: 0.0000 | L1_Loss: 5.2223 | Eps: 0.736\n",
      "Ep 160/500 | Avg Reward: -28.26 | Avg Length: 96.0 | L0_Loss: 0.0000 | L1_Loss: 5.0032 | Eps: 0.698\n",
      "Ep 180/500 | Avg Reward: -31.48 | Avg Length: 93.7 | L0_Loss: 0.0000 | L1_Loss: 5.4326 | Eps: 0.660\n",
      "Ep 200/500 | Avg Reward: -32.80 | Avg Length: 90.8 | L0_Loss: 0.0000 | L1_Loss: 5.6567 | Eps: 0.622\n",
      "Ep 220/500 | Avg Reward: -37.11 | Avg Length: 94.5 | L0_Loss: 0.0000 | L1_Loss: 5.4299 | Eps: 0.584\n",
      "Ep 240/500 | Avg Reward: -38.55 | Avg Length: 94.5 | L0_Loss: 0.0000 | L1_Loss: 6.1437 | Eps: 0.546\n",
      "Ep 260/500 | Avg Reward: -38.44 | Avg Length: 89.7 | L0_Loss: 0.0000 | L1_Loss: 6.3272 | Eps: 0.508\n",
      "Ep 280/500 | Avg Reward: -44.21 | Avg Length: 94.2 | L0_Loss: 0.0000 | L1_Loss: 6.5458 | Eps: 0.470\n",
      "Ep 300/500 | Avg Reward: -46.71 | Avg Length: 96.0 | L0_Loss: 0.0000 | L1_Loss: 7.8059 | Eps: 0.432\n",
      "Ep 320/500 | Avg Reward: -52.61 | Avg Length: 96.0 | L0_Loss: 0.0000 | L1_Loss: 7.5092 | Eps: 0.394\n",
      "Ep 340/500 | Avg Reward: -56.40 | Avg Length: 97.3 | L0_Loss: 0.0000 | L1_Loss: 6.9340 | Eps: 0.356\n",
      "Ep 360/500 | Avg Reward: -59.85 | Avg Length: 98.5 | L0_Loss: 0.0000 | L1_Loss: 8.1211 | Eps: 0.318\n",
      "Ep 380/500 | Avg Reward: -59.55 | Avg Length: 97.0 | L0_Loss: 0.0000 | L1_Loss: 7.7241 | Eps: 0.280\n",
      "Ep 400/500 | Avg Reward: -66.44 | Avg Length: 98.8 | L0_Loss: 0.0000 | L1_Loss: 8.2734 | Eps: 0.242\n",
      "Ep 420/500 | Avg Reward: -71.74 | Avg Length: 99.5 | L0_Loss: 0.0000 | L1_Loss: 8.2735 | Eps: 0.204\n",
      "Ep 440/500 | Avg Reward: -76.95 | Avg Length: 99.5 | L0_Loss: 0.0000 | L1_Loss: 8.2018 | Eps: 0.166\n",
      "Ep 460/500 | Avg Reward: -81.02 | Avg Length: 99.6 | L0_Loss: 0.0000 | L1_Loss: 8.3328 | Eps: 0.128\n",
      "Ep 480/500 | Avg Reward: -81.51 | Avg Length: 100.0 | L0_Loss: 0.0000 | L1_Loss: 9.4997 | Eps: 0.090\n",
      "Ep 500/500 | Avg Reward: -84.66 | Avg Length: 98.7 | L0_Loss: 0.0000 | L1_Loss: 9.8629 | Eps: 0.052\n",
      "Training Finished (HAC).\n"
     ]
    }
   ],
   "source": [
    "# Training Loop\n",
    "print(\"Starting HAC Training...\")\n",
    "\n",
    "for i_episode in range(1, NUM_EPISODES_HAC + 1):\n",
    "    # Reset env and get initial state (normalized)\n",
    "    initial_state_raw = hac_agent.env.reset()\n",
    "    current_state_norm = hac_agent.env.get_normalized_state()\n",
    "\n",
    "    episode_env_reward = 0.0\n",
    "    episode_steps = 0\n",
    "    ep_low_loss = 0.0\n",
    "    ep_high_loss = 0.0\n",
    "    num_low_updates = 0\n",
    "    num_high_updates = 0\n",
    "\n",
    "    # Start the hierarchical process from the top level (Level 1)\n",
    "    # Pass the final goal to the top level\n",
    "    transitions, ep_env_r, _, _ = hac_agent.run_level(\n",
    "        level=1,\n",
    "        initial_state_norm=current_state_norm,\n",
    "        goal_norm=hac_agent.final_goal_norm\n",
    "    )\n",
    "\n",
    "    # After the top level call finishes (episode ends or timeout)\n",
    "    episode_env_reward = ep_env_r  # Total env reward accumulated\n",
    "    episode_steps = hac_agent.total_steps  # Get total primitive steps taken\n",
    "    hac_agent.total_steps = 0  # Reset agent's internal step counter if needed (or manage globally)\n",
    "\n",
    "    # Store and update using the collected transitions from the top-level call\n",
    "    # (which includes low-level transitions nested within)\n",
    "    # Need to modify store_and_update or process transitions here\n",
    "\n",
    "    l0_loss, l1_loss = hac_agent.store_and_update(transitions)\n",
    "    ep_low_loss = l0_loss\n",
    "    ep_high_loss = l1_loss\n",
    "\n",
    "    # --- Logging ---\n",
    "    hac_episode_rewards.append(episode_env_reward)\n",
    "    hac_episode_lengths.append(episode_steps)  # Log total primitive steps\n",
    "    hac_episode_epsilons.append(hac_agent.current_epsilon)\n",
    "    hac_low_level_losses.append(ep_low_loss)\n",
    "    hac_high_level_losses.append(ep_high_loss)\n",
    "\n",
    "    if i_episode % 20 == 0:\n",
    "        avg_reward = np.mean(hac_episode_rewards[-20:])\n",
    "        avg_length = np.mean(hac_episode_lengths[-20:])\n",
    "        avg_ll_loss = np.mean(hac_low_level_losses[-20:])\n",
    "        avg_hl_loss = np.mean(hac_high_level_losses[-20:])\n",
    "        print(\n",
    "            f\"Ep {i_episode}/{NUM_EPISODES_HAC} | Avg Reward: {avg_reward:.2f} | Avg Length: {avg_length:.1f} | L0_Loss: {avg_ll_loss:.4f} | L1_Loss: {avg_hl_loss:.4f} | Eps: {hac_agent.current_epsilon:.3f}\")\n",
    "\n",
    "    # Update epsilon\n",
    "    hac_agent.current_epsilon = max(hac_agent.epsilon_end, hac_agent.epsilon_start - i_episode / NUM_EPISODES_HAC * (\n",
    "                hac_agent.epsilon_start - hac_agent.epsilon_end))\n",
    "\n",
    "print(\"Training Finished (HAC).\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing the Learning Process\n",
    "\n",
    "Plot overall episode rewards and lengths, and optionally the losses for each level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\faree\\AppData\\Local\\Temp\\ipykernel_11292\\1371273262.py:42: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  plt.yscale('log')\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x800 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting results for HAC\n",
    "plt.figure(figsize=(18, 8))\n",
    "\n",
    "# Episode Rewards (Environment Reward)\n",
    "plt.subplot(2, 3, 1)\n",
    "plt.plot(hac_episode_rewards)\n",
    "plt.title('HAC GridWorld: Episode Env Rewards')\n",
    "plt.xlabel('Episode')\n",
    "plt.ylabel('Total Env Reward')\n",
    "plt.grid(True)\n",
    "if len(hac_episode_rewards) >= 50:\n",
    "    rewards_ma_hac = np.convolve(hac_episode_rewards, np.ones(50)/50, mode='valid')\n",
    "    plt.plot(np.arange(len(rewards_ma_hac)) + 49, rewards_ma_hac, label='50-episode MA', color='orange')\n",
    "    plt.legend()\n",
    "\n",
    "# Episode Lengths (Primitive Steps)\n",
    "plt.subplot(2, 3, 2)\n",
    "plt.plot(hac_episode_lengths)\n",
    "plt.title('HAC GridWorld: Episode Lengths (Primitive Steps)')\n",
    "plt.xlabel('Episode')\n",
    "plt.ylabel('Steps')\n",
    "plt.grid(True)\n",
    "if len(hac_episode_lengths) >= 50:\n",
    "    lengths_ma_hac = np.convolve(hac_episode_lengths, np.ones(50)/50, mode='valid')\n",
    "    plt.plot(np.arange(len(lengths_ma_hac)) + 49, lengths_ma_hac, label='50-episode MA', color='orange')\n",
    "    plt.legend()\n",
    "\n",
    "# Epsilon\n",
    "plt.subplot(2, 3, 3)\n",
    "plt.plot(hac_episode_epsilons)\n",
    "plt.title('HAC: Epsilon Decay')\n",
    "plt.xlabel('Episode')\n",
    "plt.ylabel('Epsilon')\n",
    "plt.grid(True)\n",
    "\n",
    "# Low Level Loss\n",
    "plt.subplot(2, 3, 4)\n",
    "plt.plot(hac_low_level_losses)\n",
    "plt.title('HAC GridWorld: Avg Low-Level Loss / Episode')\n",
    "plt.xlabel('Episode')\n",
    "plt.ylabel('Avg MSE Loss (L0)')\n",
    "plt.yscale('log')\n",
    "plt.grid(True, which='both')\n",
    "if len(hac_low_level_losses) >= 50:\n",
    "    ll_loss_ma = np.convolve(hac_low_level_losses, np.ones(50)/50, mode='valid')\n",
    "    valid_indices = np.where(ll_loss_ma > 1e-8)[0]\n",
    "    if len(valid_indices) > 0:\n",
    "        plt.plot(np.arange(len(ll_loss_ma))[valid_indices] + 49, ll_loss_ma[valid_indices], label='50-episode MA', color='orange')\n",
    "        plt.legend()\n",
    "\n",
    "# High Level Loss\n",
    "plt.subplot(2, 3, 5)\n",
    "plt.plot(hac_high_level_losses)\n",
    "plt.title('HAC GridWorld: Avg High-Level Loss / Episode')\n",
    "plt.xlabel('Episode')\n",
    "plt.ylabel('Avg MSE Loss (L1)')\n",
    "plt.yscale('log')\n",
    "plt.grid(True, which='both')\n",
    "if len(hac_high_level_losses) >= 50:\n",
    "    hl_loss_ma = np.convolve(hac_high_level_losses, np.ones(50)/50, mode='valid')\n",
    "    valid_indices = np.where(hl_loss_ma > 1e-8)[0]\n",
    "    if len(valid_indices) > 0:\n",
    "        plt.plot(np.arange(len(hl_loss_ma))[valid_indices] + 49, hl_loss_ma[valid_indices], label='50-episode MA', color='orange')\n",
    "        plt.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analysis of HAC Learning Curves:**\n",
    "\n",
    "1.  **Environment Rewards & Lengths:** These reflect the overall task success. Expect rewards to increase and lengths to decrease as both levels learn. Learning might be slower initially compared to simpler methods due to the complexity but should eventually solve longer-horizon problems more effectively.\n",
    "2.  **Level Losses:** The losses for both low-level ($L_0$) and high-level ($L_1$) Q-networks should decrease, indicating that they are learning their respective goal-conditioned value functions from the (potentially hindsight-relabeled) transitions.\n",
    "3.  **Epsilon Decay:** Standard decay.\n",
    "\n",
    "The key indicator is whether the agent successfully learns to use subgoals to reach the final goal more efficiently than a flat agent would, especially if the task were made more complex (larger grid, sparse rewards)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyzing the Learned Policies (Testing)\n",
    "\n",
    "Test the agent by running the hierarchical policy stack."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- Testing HAC Agent (5 episodes) ---\n",
      "Test Episode 1: Reward = -91.90, Length = 100\n",
      "Test Episode 2: Reward = -91.00, Length = 100\n",
      "Test Episode 3: Reward = -91.90, Length = 100\n",
      "Test Episode 4: Reward = -91.90, Length = 100\n",
      "Test Episode 5: Reward = -91.90, Length = 100\n",
      "--- Testing Complete. Average Reward: -91.72, Avg Length: 100.0 ---\n"
     ]
    }
   ],
   "source": [
    "def test_hac_agent(agent: HACAgent, \n",
    "                   env_instance: GridEnvironmentHAC, \n",
    "                   num_episodes: int = 5, \n",
    "                   seed_offset: int = 7000) -> None:\n",
    "    \"\"\" Tests the trained HAC agent. \"\"\"\n",
    "    print(f\"\\n--- Testing HAC Agent ({num_episodes} episodes) ---\")\n",
    "    all_episode_rewards = []\n",
    "    all_episode_lengths = []\n",
    "\n",
    "    # Set epsilon low for testing (greedy policies at both levels)\n",
    "    original_epsilon = agent.current_epsilon\n",
    "    agent.current_epsilon = 0.01 # Small epsilon for minimal exploration\n",
    "\n",
    "    for i in range(num_episodes):\n",
    "        initial_state_raw = env_instance.reset()\n",
    "        current_state_norm = env_instance.get_normalized_state()\n",
    "        \n",
    "        episode_env_reward = 0.0\n",
    "        total_primitive_steps = 0\n",
    "        agent.total_steps = 0 # Reset internal counter if used\n",
    "        \n",
    "        # Run the top level (which recursively calls lower levels)\n",
    "        _, ep_env_r, _, final_state = agent.run_level(\n",
    "            level=1, \n",
    "            initial_state_norm=current_state_norm,\n",
    "            goal_norm=agent.final_goal_norm\n",
    "        )\n",
    "        \n",
    "        episode_env_reward = ep_env_r\n",
    "        # Need a way to get total primitive steps from run_level or track externally\n",
    "        # For now, let's assume the agent class tracks it or we approximate\n",
    "        total_primitive_steps = agent.total_steps # Assuming agent tracks this\n",
    "\n",
    "        print(f\"Test Episode {i+1}: Reward = {episode_env_reward:.2f}, Length = {total_primitive_steps}\")\n",
    "        all_episode_rewards.append(episode_env_reward)\n",
    "        all_episode_lengths.append(total_primitive_steps)\n",
    "\n",
    "    # Restore original epsilon\n",
    "    agent.current_epsilon = original_epsilon\n",
    "    print(f\"--- Testing Complete. Average Reward: {np.mean(all_episode_rewards):.2f}, Avg Length: {np.mean(all_episode_lengths):.1f} ---\")\n",
    "\n",
    "# Run test episodes\n",
    "test_hac_agent(hac_agent, hac_env_run, num_episodes=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Common Challenges and Extensions of HAC\n",
    "\n",
    "**Challenge: Goal Representation**\n",
    "*   **Problem:** Defining effective subgoals (actions for higher levels) is crucial but non-trivial. Using absolute states (like grid cells) can be limiting.\n",
    "*   **Solutions**:\n",
    "    *   **Relative Goals:** Define goals as desired *changes* in state.\n",
    "    *   **Learned Goal Spaces:** Learn an appropriate embedding space for goals.\n",
    "\n",
    "**Challenge: Subgoal Feasibility & Non-Stationarity**\n",
    "*   **Problem:** The high level might set subgoals that the low level cannot currently achieve, wasting time. Also, as the low level learns, the high level's task (selecting good subgoals) changes (non-stationarity).\n",
    "*   **Solutions**:\n",
    "    *   **Goal Filtering:** High level only proposes goals deemed achievable by the low level (e.g., based on value estimates).\n",
    "    *   **Coordinated Training:** Careful scheduling of updates between levels.\n",
    "\n",
    "**Challenge: Time Limit ($H$) Tuning**\n",
    "*   **Problem:** Setting the right time limit $H$ is difficult. Too short, and subgoals are rarely achieved; too long, and learning becomes slow and less hierarchical.\n",
    "   **Solutions**:\n",
    "    *   **Adaptive $H$:** Try to learn or adapt $H$ based on task complexity or success rates.\n",
    "    *   **Remove Fixed Limit:** Some HRL methods use termination conditions other than fixed time limits.\n",
    "\n",
    "**Challenge: Complexity & Hyperparameters**\n",
    "*   **Problem:** HRL methods like HAC introduce many new design choices and hyperparameters (number of levels, goal space, intrinsic rewards, time limits, hindsight strategy) on top of the base RL algorithm's parameters.\n",
    "   **Solution:** Careful implementation, systematic tuning, starting with settings from literature.\n",
    "\n",
    "**Challenge: Hindsight Implementation Details**\n",
    "*   **Problem:** Correctly implementing the storage and sampling of hindsight goals (final vs. future strategies, how rewards/dones are set for hindsight transitions) is critical and can be subtle.\n",
    "   **Solution:** Follow standard HER/HAC practices carefully.\n",
    "\n",
    "**Extensions:**\n",
    "- **HIRO:** Uses relative goals.\n",
    "- **RIG:** Learns goal representations.\n",
    "- **Feudal Networks:** Earlier HRL approach with manager/worker structure.\n",
    "\n",
    "## Conclusion\n",
    "\n",
    "Hierarchical Actor-Critic (HAC) provides a framework for tackling complex, long-horizon reinforcement learning tasks by introducing multiple levels of control. Higher levels set goals (subgoals) for lower levels, which in turn execute actions to achieve them. Key mechanisms like goal-conditioning, fixed time limits per level, intrinsic rewards for subgoal achievement, and hindsight goal relabeling enable learning in challenging scenarios, particularly those with sparse rewards.\n",
    "\n",
    "While significantly more complex to implement and tune than flat RL algorithms, HAC and other HRL methods offer a promising direction for improving exploration, sample efficiency, and the ability to solve problems requiring reasoning over extended time scales. The core idea of decomposing tasks and learning goal-conditioned policies remains a central theme in advanced reinforcement learning research."
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